Intrinsic Light Field Decomposition and Disparity Estimation with Deep Encoder-Decoder Network

Abstract

We present an encoder-decoder deep neural network that solves non-Lambertian intrinsic light field decomposition, where we recover all three intrinsic components: albedo, shading, and specularity. We learn a sparse set of features from 3D epipolar volumes and use them in separate decoder pathways to reconstruct intrinsic light fields. While being trained on synthetic data generated with Blender, our model still generalizes to real world examples captured with a Lytro Illum plenoptic camera. The proposed method outperforms state-of-the-art approaches for single images and achieves competitive accuracy with recent modeling methods for light fields.